[1] [1] LIU B Y, CHENG J R, TANG X Y, et al. Moving vehicles recognition in complex dynamic environment [J]. Journal of Frontiers of Computer Science & Technology, 2017, 11(1): 134-143. (in Chinese)

[2] [2] LI H, FU K, YAN M L, et al. Vehicle detection in remote sensing images using denoizing-based convolutional neural networks [J]. Remote Sensing Letters, 2017, 8(3): 262-270.

[3] [3] WU Y F, WANG Y J, SUN H J, et al. LSS-target detection in complex sky backgrounds [J]. Chinese Optics, 2019, 12(4): 853-865. (in Chinese)

[4] [4] GAO W, ZHU M, HE B G,et al. Overview of target tracking technology [J]. Chinese Optics, 2014, 7(3): 365-375.

[5] [5] ZHAO Y Q, RAO Y, DONG S P, et al. Survey on deep learning object detection [J]. Journal of Image and Graphics, 2020, 25(4):629-654. (in Chinese)

[6] [6] JIANG W T, ZHANG C, ZHANG S C, et al. Multiscale feature map fusion algorithm for target detection [J]. Journal of Image and Graphics, 2019, 24(11):1918-1931. (in Chinese)

[7] [7] YANG D, DAI F. Improved ViBe algorithm for detection of moving objects [J]. Journal of Image and Graphics, 2018, 23(12):1813-1828. (in Chinese)

[8] [8] DONG X X, HE X H, WU X H, et al. Object detection algorithm based on attention mask fusion [J]. Chinese Journal of Liquid Crystals and Displays, 2019, 34(8): 825-833. (in Chinese)

[9] [9] GE W, SHI Z W. Application of improved YoLOV3 algorithm in pedestrian identification [J]. Computer Engineering and Applications, 2019, 55(20): 128-133. (in Chinese)

[10] [10] LIANG H, SONG Y L, QIAN F, et al. Detection of small target in aerial photography based on deep learning [J]. Chinese Journal of Liquid Crystals and Displays, 2018, 33(9): 793-800. (in Chinese)

[11] [11] LI Y P, HOU L Y, WANG C. Moving objects detection in automatic driving based on YOLOv3 [J]. Computer Engineering and Design, 2019, 40(4): 1139-1144. (in Chinese)

[12] [12] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection [C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 779-788.

[13] [13] REDMON J, FARHADI A. YOLO9000: better, faster, stronger [C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 6517-6525.

[14] [14] DAI J F, LI Y, HE K M, et al. R-FCN: object detection via region-based fully convolutional networks [C]//Proceedings of the 30th International Conference on Neural Information Processing Systems. Barcelona: ACM, 2016: 379-387.

[15] [15] HUANG G, LIU Z, VAN DER MAATEN L, et al. Densely connected convolutional networks [C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 2261-2269.

[16] [16] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition [C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 770-778.